Barriers and facilitators of habit building for long-term adherence to antihypertensive therapy among people with hypertensive disorders in Los Angeles, California: a qualitative study.

OBJECTIVES: The aim of this study was to a) explore barriers and facilitators associated with medication-taking habit formation, and b) elicit feedback on the components of an intervention designed to help form strong habits for long-term medication adherence. DESIGN: The study design was qualitative; we conducted semistructured interviews between September 2021 and February 2022. SETTING: The interviews were conducted online, with 27 participants recruited at the Cedars-Sinai Medical Center in Los Angeles, California. PARTICIPANTS: A purposive sample of 20 patients who were over 18 years of age, had been diagnosed with hypertensive disorder (or reported high blood pressure; >140/90 mm Hg) and who were prescribed antihypertensive therapy at the time of recruitment, along with seven providers were interviewed. RESULTS: Contextual factors included frequent changes to prescription for regimen adjustment, and polypharmacy. Forgetfulness, perceived need for medication, and routine disruptions were identified as possible barriers to habit formation. Facilitators of habit formation included identification of stable routines for anchoring, planning, use of external reminders (including visual reminders) and pillboxes for prescription management, and extrinsic motivation for forming habits. Interestingly, experiencing medication side effects was identified as a possible barrier and a possible facilitator of habit formation. Feedback on study components included increasing text size, and visual appeal of the habit leaflet; and imparting variation in text message content and adjusting their frequency to once a day. Patients generally favoured the use of conditional financial incentives to support habit formation. CONCLUSION: The study sheds light on some key considerations concerning the contextual factors for habit formation among people with hypertension. As such, future studies may evaluate the generalisability of our findings, consider the role of visual reminders in habit formation and sustenance, and explore possible disruptions to habits. TRIAL REGISTRATION NUMBER: NCT04029883.

Polymorphic ventricular tachycardia and cardiac arrest from abiraterone-induced hypokalemia: a case report.

BACKGROUND: Polymorphic ventricular tachycardia (PMVT) is an unstable and often fatal cardiac tachyarrhythmia. While there are many causes of this rhythm, including electrolyte imbalances, ischemia, and genetic disorders, iatrogenic etiologies are important to recognize. Abiraterone is an androgen synthesis antagonist effective in treating prostate cancer, but here we describe a case of severe hypokalemia secondary to abiraterone resulting in polymorphic ventricular tachycardia and cardiac arrest. While this is a potential adverse effect of the medication, severe hypokalemia causing polymorphic ventricular tachycardia and cardiac arrest, as seen in our patient's case, has not been described. CASE PRESENTATION: A 78-year-old African-American man with history of prostate cancer presents with polymorphic ventricular tachycardia and cardiac arrest. After resuscitation, he was found to be severely hypokalemic and refractory to large doses of repletion. Evaluation of secondary causes of hypokalemia identified the likely culprit to be adverse effects from prostate cancer treatment. CONCLUSION: A broad differential diagnosis for polymorphic ventricular tachycardia is essential in identifying and treating patients presenting in this rhythm. Here we present a case of iatrogenic polymorphic ventricular tachycardia secondary to oncologic treatment.

Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study.

BACKGROUND: Preoperative risk assessments used in clinical practice are insufficient in their ability to identify risk for postoperative mortality. Deep-learning analysis of electrocardiography can identify hidden risk markers that can help to prognosticate postoperative mortality. We aimed to develop a prognostic model that accurately predicts postoperative mortality in patients undergoing medical procedures and who had received preoperative electrocardiographic diagnostic testing. METHODS: In a derivation cohort of preoperative patients with available electrocardiograms (ECGs) from Cedars-Sinai Medical Center (Los Angeles, CA, USA) between Jan 1, 2015 and Dec 31, 2019, a deep-learning algorithm was developed to leverage waveform signals to discriminate postoperative mortality. We randomly split patients (8:1:1) into subsets for training, internal validation, and final algorithm test analyses. Model performance was assessed using area under the receiver operating characteristic curve (AUC) values in the hold-out test dataset and in two external hospital cohorts and compared with the established Revised Cardiac Risk Index (RCRI) score. The primary outcome was post-procedural mortality across three health-care systems. FINDINGS: 45 969 patients had a complete ECG waveform image available for at least one 12-lead ECG performed within the 30 days before the procedure date (59 975 inpatient procedures and 112 794 ECGs): 36 839 patients in the training dataset, 4549 in the internal validation dataset, and 4581 in the internal test dataset. In the held-out internal test cohort, the algorithm discriminates mortality with an AUC value of 0·83 (95% CI 0·79-0·87), surpassing the discrimination of the RCRI score with an AUC of 0·67 (0·61-0·72). The algorithm similarly discriminated risk for mortality in two independent US health-care systems, with AUCs of 0·79 (0·75-0·83) and 0·75 (0·74-0·76), respectively. Patients determined to be high risk by the deep-learning model had an unadjusted odds ratio (OR) of 8·83 (5·57-13·20) for postoperative mortality compared with an unadjusted OR of 2·08 (0·77-3·50) for postoperative mortality for RCRI scores of more than 2. The deep-learning algorithm performed similarly for patients undergoing cardiac surgery (AUC 0·85 [0·77-0·92]), non-cardiac surgery (AUC 0·83 [0·79-0·88]), and catheterisation or endoscopy suite procedures (AUC 0·76 [0·72-0·81]). INTERPRETATION: A deep-learning algorithm interpreting preoperative ECGs can improve discrimination of postoperative mortality. The deep-learning algorithm worked equally well for risk stratification of cardiac surgeries, non-cardiac surgeries, and catheterisation laboratory procedures, and was validated in three independent health-care systems. This algorithm can provide additional information to clinicians making the decision to perform medical procedures and stratify the risk of future complications. FUNDING: National Heart, Lung, and Blood Institute.

A Paratope-Enhanced Method to Determine Breadth and Depth TCR Clonal Metrics of the Private Human T-Cell Vaccine Response after SARS-CoV-2 Vaccination.

Quantitative metrics for vaccine-induced T-cell responses are an important need for developing correlates of protection and their use in vaccine-based medical management and population health. Molecular TCR analysis is an appealing strategy but currently requires a targeted methodology involving complex integration of ex vivo data (antigen-specific functional T-cell cytokine responses and TCR molecular responses) that uncover only public antigen-specific metrics. Here, we describe an untargeted private TCR method that measures breadth and depth metrics of the T-cell response to vaccine challenge using a simple pre- and post-vaccine subject sampling, TCR immunoseq analysis, and a bioinformatic approach using self-organizing maps and GLIPH2. Among 515 subjects undergoing SARS-CoV-2 mRNA vaccination, we found that breadth and depth metrics were moderately correlated between the targeted public TCR response and untargeted private TCR response methods. The untargeted private TCR method was sufficiently sensitive to distinguish subgroups of potential clinical significance also observed using public TCR methods (the reduced T-cell vaccine response with age and the paradoxically elevated T-cell vaccine response of patients on anti-TNF immunotherapy). These observations suggest the promise of this untargeted private TCR method to produce T-cell vaccine-response metrics in an antigen-agnostic and individual-autonomous context.

Cardiac microstructural alterations in immune-inflammatory myocardial disease: a retrospective case-control study.

BACKGROUND: Immune-inflammatory myocardial disease contributes to multiple chronic cardiac processes, but access to non-invasive screening is limited. We have previously developed a method of echocardiographic texture analysis, called the high-spectrum signal intensity coefficient (HS-SIC) which assesses myocardial microstructure and previously associated with myocardial fibrosis. We aimed to determine whether this echocardiographic texture analysis of cardiac microstructure can identify inflammatory cardiac disease in the clinical setting. METHODS: We conducted a retrospective case-control study of 318 patients with distinct clinical myocardial pathologies and 20 healthy controls. Populations included myocarditis, atypical chest pain/palpitations, STEMI, severe aortic stenosis, acute COVID infection, amyloidosis, and cardiac transplantation with acute rejection, without current rejection but with prior rejection, and with no history of rejection. We assessed the HS-SIC's ability to differentiate between a broader diversity of clinical groups and healthy controls. We used Kruskal-Wallis tests to compare HS-SIC values measured in each of the clinical populations with those in the healthy control group and compared HS-SIC values between the subgroups of cardiac transplantation rejection status. RESULTS: For the total sample of N = 338, the mean age was 49.6 ± 20.9 years and 50% were women. The mean ± standard error of the mean of HS-SIC were: 0.668 ± 0.074 for controls, 0.552 ± 0.049 for atypical chest pain/palpitations, 0.425 ± 0.058 for myocarditis, 0.881 ± 0.129 for STEMI, 1.116 ± 0.196 for severe aortic stenosis, 0.904 ± 0.116 for acute COVID, and 0.698 ± 0.103 for amyloidosis. Among cardiac transplant recipients, HS-SIC values were 0.478 ± 0.999 for active rejection, 0.594 ± 0.091 for prior rejection, and 1.191 ± 0.442 for never rejection. We observed significant differences in HS-SIC between controls and myocarditis (P = 0.0014), active rejection (P = 0.0076), and atypical chest pain or palpitations (P = 0.0014); as well as between transplant patients with active rejection and those without current or prior rejection (P = 0.031). CONCLUSIONS: An echocardiographic method can be used to characterize tissue signatures of microstructural changes across a spectrum of cardiac disease including immune-inflammatory conditions.

High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy With Cardiovascular Deep Learning.

IMPORTANCE: Early detection and characterization of increased left ventricular (LV) wall thickness can markedly impact patient care but is limited by under-recognition of hypertrophy, measurement error and variability, and difficulty differentiating causes of increased wall thickness, such as hypertrophy, cardiomyopathy, and cardiac amyloidosis. OBJECTIVE: To assess the accuracy of a deep learning workflow in quantifying ventricular hypertrophy and predicting the cause of increased LV wall thickness. DESIGN, SETTINGS, AND PARTICIPANTS: This cohort study included physician-curated cohorts from the Stanford Amyloid Center and Cedars-Sinai Medical Center (CSMC) Advanced Heart Disease Clinic for cardiac amyloidosis and the Stanford Center for Inherited Cardiovascular Disease and the CSMC Hypertrophic Cardiomyopathy Clinic for hypertrophic cardiomyopathy from January 1, 2008, to December 31, 2020. The deep learning algorithm was trained and tested on retrospectively obtained independent echocardiogram videos from Stanford Healthcare, CSMC, and the Unity Imaging Collaborative. MAIN OUTCOMES AND MEASURES: The main outcome was the accuracy of the deep learning algorithm in measuring left ventricular dimensions and identifying patients with increased LV wall thickness diagnosed with hypertrophic cardiomyopathy and cardiac amyloidosis. RESULTS: The study included 23 745 patients: 12 001 from Stanford Health Care (6509 [54.2%] female; mean [SD] age, 61.6 [17.4] years) and 1309 from CSMC (808 [61.7%] female; mean [SD] age, 62.8 [17.2] years) with parasternal long-axis videos and 8084 from Stanford Health Care (4201 [54.0%] female; mean [SD] age, 69.1 [16.8] years) and 2351 from CSMS (6509 [54.2%] female; mean [SD] age, 69.6 [14.7] years) with apical 4-chamber videos. The deep learning algorithm accurately measured intraventricular wall thickness (mean absolute error [MAE], 1.2 mm; 95% CI, 1.1-1.3 mm), LV diameter (MAE, 2.4 mm; 95% CI, 2.2-2.6 mm), and posterior wall thickness (MAE, 1.4 mm; 95% CI, 1.2-1.5 mm) and classified cardiac amyloidosis (area under the curve [AUC], 0.83) and hypertrophic cardiomyopathy (AUC, 0.98) separately from other causes of LV hypertrophy. In external data sets from independent domestic and international health care systems, the deep learning algorithm accurately quantified ventricular parameters (domestic: R2, 0.96; international: R2, 0.90). For the domestic data set, the MAE was 1.7 mm (95% CI, 1.6-1.8 mm) for intraventricular septum thickness, 3.8 mm (95% CI, 3.5-4.0 mm) for LV internal dimension, and 1.8 mm (95% CI, 1.7-2.0 mm) for LV posterior wall thickness. For the international data set, the MAE was 1.7 mm (95% CI, 1.5-2.0 mm) for intraventricular septum thickness, 2.9 mm (95% CI, 2.4-3.3 mm) for LV internal dimension, and 2.3 mm (95% CI, 1.9-2.7 mm) for LV posterior wall thickness. The deep learning algorithm accurately detected cardiac amyloidosis (AUC, 0.79) and hypertrophic cardiomyopathy (AUC, 0.89) in the domestic external validation site. CONCLUSIONS AND RELEVANCE: In this cohort study, the deep learning model accurately identified subtle changes in LV wall geometric measurements and the causes of hypertrophy. Unlike with human experts, the deep learning workflow is fully automated, allowing for reproducible, precise measurements, and may provide a foundation for precision diagnosis of cardiac hypertrophy.

Longitudinal SARS-CoV-2 mRNA Vaccine-Induced Humoral Immune Responses in Patients with Cancer.

Longitudinal studies of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine-induced immune responses in patients with cancer are needed to optimize clinical care. In a prospective cohort study of 366 (291 vaccinated) patients, we measured antibody levels [anti-spike (IgG-(S-RBD) and anti-nucleocapsid immunoglobulin] at three time points. Antibody level trajectories and frequency of breakthrough infections were evaluated by tumor type and timing of treatment relative to vaccination. IgG-(S-RBD) at peak response (median = 42 days after dose 2) was higher (P = 0.002) and remained higher after 4 to 6 months (P = 0.003) in patients receiving mRNA-1273 compared with BNT162b2. Patients with solid tumors attained higher peak levels (P = 0.001) and sustained levels after 4 to 6 months (P < 0.001) compared with those with hematologic malignancies. B-cell targeted treatment reduced peak (P = 0.001) and sustained antibody responses (P = 0.003). Solid tumor patients receiving immune checkpoint inhibitors before vaccination had lower sustained antibody levels than those who received treatment after vaccination (P = 0.043). Two (0.69%) vaccinated and one (1.9%) unvaccinated patient had severe COVID-19 illness during follow-up. Our study shows variation in sustained antibody responses across cancer populations receiving various therapeutic modalities, with important implications for vaccine booster timing and patient selection. SIGNIFICANCE: Long-term studies of immunogenicity of SARS-CoV-2 vaccines in patients with cancer are needed to inform evidence-based guidelines for booster vaccinations and to tailor sequence and timing of vaccinations to elicit improved humoral responses.

Recipient and surgical factors trigger severe primary graft dysfunction after heart transplant.

BACKGROUND: Primary graft dysfunction (PGD) is a major cause of early mortality following heart transplant (HT). The International Society for Heart and Lung Transplantation (ISHLT) subdivides PGD into 3 grades of increasing severity. Most studies have assessed risk factors for PGD without distinguishing between PGD severity grade. We sought to identify recipient, donor and surgical risk factors specifically associated with mild/moderate or severe PGD. METHODS: We identified 734 heart transplant recipients at our institution transplanted between January 1, 2012 and December 31, 2018. PGD was defined according to modified ISHLT criteria. Recipient, donor and surgical variables were analyzed by multinomial logistic regression with mild/moderate or severe PGD as the response. Variables significant in single variable modeling were subject to multivariable analysis via penalized logistic regression. RESULTS: PGD occurred in 24% of the cohort (n = 178) of whom 6% (n = 44) had severe PGD. One-year survival was reduced in recipients with severe PGD but not in those with mild or moderate PGD. Multivariable analysis identified 3 recipient factors: prior cardiac surgery, recipient treatment with ACEI/ARB/ARNI plus MRA, recipient treatment with amiodarone plus beta-blocker, and 3 surgical factors: longer ischemic time, more red blood cell transfusions, and more platelet transfusions, that were associated with severe PGD. We developed a clinical risk score, ABCE, which provided acceptable discrimination and calibration for severe PGD. CONCLUSIONS: Risk factors for mild/moderate PGD were largely distinct from those for severe PGD, suggesting a differing pathophysiology involving several biological pathways. Further research into mechanisms underlying the development of PGD is urgently needed.

A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity.

Worldwide, testing capacity for SARS-CoV-2 is limited and bottlenecks in the scale up of polymerase chain reaction (PCR-based testing exist. Our aim was to develop and evaluate a machine learning algorithm to diagnose COVID-19 in the inpatient setting. The algorithm was based on basic demographic and laboratory features to serve as a screening tool at hospitals where testing is scarce or unavailable. We used retrospectively collected data from the UCLA Health System in Los Angeles, California. We included all emergency room or inpatient cases receiving SARS-CoV-2 PCR testing who also had a set of ancillary laboratory features (n = 1,455) between 1 March 2020 and 24 May 2020. We tested seven machine learning models and used a combination of those models for the final diagnostic classification. In the test set (n = 392), our combined model had an area under the receiver operator curve of 0.91 (95% confidence interval 0.87-0.96). The model achieved a sensitivity of 0.93 (95% CI 0.85-0.98), specificity of 0.64 (95% CI 0.58-0.69). We found that our machine learning algorithm had excellent diagnostic metrics compared to SARS-CoV-2 PCR. This ensemble machine learning algorithm to diagnose COVID-19 has the potential to be used as a screening tool in hospital settings where PCR testing is scarce or unavailable.

Design, Challenges, and Implications of Quality Improvement Projects Using the Electronic Medical Record: Case Study: A Protocol to Reduce the Burden of Postoperative Atrial Fibrillation.

Postoperative atrial fibrillation (POAF) is a frequent complication of cardiac surgery, which results in increased morbidity, mortality, length of stay, and hospital costs. We developed and followed a process map to implement a protocol to decrease POAF: (1) identify stakeholders and form a working committee, (2) formal literature and guideline review, (3) retrospective analysis of current institutional data, (4) data modeling to determine expected effects of change, (4) protocol development and implementation into the electronic medical record, and (5) ongoing review of data and protocol adjustment. Retrospective analysis demonstrated that POAF occurred in 29.8% of all cardiovascular surgery cases. Median length of stay was 2 days longer (P<0.001), and median total variable costs $2495 higher (P<0.001) in POAF patients. Modeling predicted that up to 60 cases of POAF and >$200 000 annually could be saved. A clinically based electronic medical record tool was implemented into the electronic medical record to aid preoperative clinic providers in identifying patients eligible for prophylactic amiodarone. Initial results during the 9-month period after implementation demonstrated a reduction in POAF in patients using the protocol, compared with those who qualified but did not receive amiodarone and those not evaluated (11.1% versus 38.7% and 38.8%; P=0.022); however, only 17.3% of patients used the protocol. A standardized methodological approach to quality improvement and electronic medical record integration has potential to significantly decrease the incidence of POAF, length of stay, and total variable cost in patients undergoing elective coronary artery bypass graft and valve surgeries. This framework for quality improvement interventions may be adapted to similar clinical problems beyond POAF.